Pitfalls and Recommended Strategies and Metrics for Suppressing Motion Artifacts in Functional MRI

Raval, Vyom; Nguyen, Kevin P.; Pinho, Marco C.; Dewey, Richard B.; Trivedi, Madhukar H.; Montillo, Albert

doi:10.1007/s12021-022-09565-8

Cited by 5 publications

(4 citation statements)

References 49 publications

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“…We found that static intrinsic FC metrics are mostly robust across motion denoising strategies in both young healthy controls and Parkinson’s participants. This is consistent with and supports previous studies 44,45 . However, dynamic FC metrics were very sensitive to denoising methods, in both groups but critically in the PD group.…”

Section: Discussionsupporting

confidence: 94%

Head motion correction shapes functional network estimates: evidence from healthy and Parkinson’s disease cohorts

Saviola

Tambalo

Cabalo

et al. 2022

Preprint

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An open discussion in studies of intrinsic brain functional connectivity is the mitigation of head motion-related artifacts, particularly in the presence of peculiar symptomatology such as in Parkinson's disease (PD). Previous studies show that Independent Component Analysis (ICA) denoising improves the reproducibility of functional connectivity findings by detecting sources of non-neural signals. However, there is still no consensus about which pre-processing pipeline should be applied in natural high motion populations such as PD, particularly in relation to novel functional network descriptions derived from dynamic connectivity analyses. In this study, we investigated how different pre-processing pipelines affect intrinsic brain connectivity metrics, both static and dynamic, derived from a group of young healthy controls (HC) and a group of PD participants. A total of 20 HC and 20 PD subjects participated in this 3 T MRI study. Resting-state functional MRI images were used to test the effects of the pre-processing pipeline of static (sFC) and temporal-varying functional connectivity (dFC) estimations. Both MRI datasets were pre-processed using three different workflows differing in the motion correction approach: (i) standard motion realignment (mc); (ii) motion outlier detection and deweighting based on image intensity change estimations (DVARS) and (iii) ICA-based noise removal using reference noise features (AROMA). Furthermore, the PD dataset was also processed with a fourth method by applying an ICA-based denoising (FIX), previously trained on the HC group. sFC analysis was performed using Group ICA, by temporally concatenating different pre-processing types in pairs of different runs. Two types of dFC analyses were considered: innovation-driven co-activation patterns (iCAPs) and co-activation patterns (CAPs). CAPs allow dFC estimations that do not require the deconvolution of the hemodynamic response function and its derivative, thus potentially being less sensitive to head-motion related noise. We found that regardless of substantial head motion differences in the two groups, sFC results were consistent across denoising strategies. Conversely, dFC was extremely sensitive to denoising strategies, particularly for the PD group with the transient-based dFC analyses. Indeed, the use of the peak-based dFC framework enables the detection of time-varying networks but in a way that is highly dependent on the motion correction pipeline. In conclusion, we show that dynamic functional network representations are highly sensitive to both head motion and to fMRI denoising methods. These findings stress the importance of considering and reporting these experimental aspects to help with the reproducibility and interpretation of different studies. Future work is needed to further investigate transient-based dFC strategies that are more robust to head motion.

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Section: Discussionsupporting

confidence: 94%

Head motion correction shapes functional network estimates: evidence from healthy and Parkinson’s disease cohorts

Saviola

Tambalo

Cabalo

et al. 2022

Preprint

View full text Add to dashboard Cite

show abstract

“…We also found that VG features were most sensitive to motion when more than 20% of the data was contaminated by framewise displacement of more than 0.2. This level of sensitivity to motion is consistent with previous research showing a strong association between functional network features and framewise motion in fMRI data (Raval et al, 2022 ). VGs are particularly sensitive to the presence of outliers and large values in the data, which is typical of what happens when there are motion events in fMRI.…”

Section: Discussionsupporting

confidence: 92%

Network Representation of fMRI Data Using Visibility Graphs: The Impact of Motion and Test-Retest Reliability

Poudel,

Sharma,

Lorenzetti

et al. 2024

Neuroinform

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Visibility graphs provide a novel approach for analysing time-series data. Graph theoretical analysis of visibility graphs can provide new features for data mining applications in fMRI. However, visibility graphs features have not been used widely in the field of neuroscience. This is likely due to a lack of understanding of their robustness in the presence of noise (e.g., motion) and their test-retest reliability. In this study, we investigated visibility graph properties of fMRI data in the human connectome project (N = 1010) and tested their sensitivity to motion and test-retest reliability. We also characterised the strength of connectivity obtained using degree synchrony of visibility graphs. We found that strong correlation (r > 0.5) between visibility graph properties, such as the number of communities and average degrees, and motion in the fMRI data. The test-retest reliability (Intraclass correlation coefficient (ICC)) of graph theoretical features was high for the average degrees (0.74, 95% CI = [0.73, 0.75]), and moderate for clustering coefficient (0.43, 95% CI = [0.41, 0.44]) and average path length (0.41, 95% CI = [0.38, 0.44]). Functional connectivity between brain regions was measured by correlating the visibility graph degrees. However, the strength of correlation was found to be moderate to low (r < 0.35). These findings suggest that even small movement in fMRI data can strongly influence robustness and reliability of visibility graph features, thus, requiring robust motion correction strategies prior to data analysis. Further studies are necessary for better understanding of the potential application of visibility graph features in fMRI.

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“…If head motion phenotype truly has no relationship with any functional connection, then QC-FC could be used a measure of validity, with lower-magnitude QC-FC indicating more effective data cleaning. Although commonly used, QC-FC has been criticized because in-scanner movement may relate to neural patterns of interest and because it does not consider signal loss ( Raval et al, 2022 ; Williams et al, 2022 ). Williams et al (2022) observed that QC-FC behaved in unexpected ways when the FD cutoff for motion scrubbing was varied.…”

Section: Methodsmentioning

confidence: 99%

Less is more: balancing noise reduction and data retention in fMRI with data-driven scrubbing

et al. 2023

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Pitfalls and Recommended Strategies and Metrics for Suppressing Motion Artifacts in Functional MRI

Abstract: Information sharing statementThis work uses publicly available datasets, which are listed with hyperlinks in the Data Availability Statement. Code used for these analyses can be accessed through the hyperlink provided in the Methods.

Cited by 5 publications

References 49 publications

Head motion correction shapes functional network estimates: evidence from healthy and Parkinson’s disease cohorts

Head motion correction shapes functional network estimates: evidence from healthy and Parkinson’s disease cohorts

Network Representation of fMRI Data Using Visibility Graphs: The Impact of Motion and Test-Retest Reliability

Less is more: balancing noise reduction and data retention in fMRI with data-driven scrubbing

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